Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery

Tasnim, Jarin; Mondal, Debajyoti

doi:10.1109/bigdata50022.2020.9378008

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A data reduction method proposed by Tasnim and Mondal [7] reduces the data size by 75% while preserving the visual elements of images. Keim et al [8,9] discussed the use of visualization techniques to explore large-scale geospatial datasets using more classical data mining methods.…”

Section: Interactive Visualizationmentioning

confidence: 99%

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

Zheng

Miao

Sankaran

2022

IJGI

View full text Add to dashboard Cite

Interpretability has attracted increasing attention in earth observation problems. We apply interactive visualization and representation analysis to guide the interpretation of glacier segmentation models. We visualize the activations from a U-Net to understand and evaluate the model performance. We built an online interface using the Shiny R package to provide comprehensive error analysis of the predictions. Users can interact with the panels and discover model failure modes. We illustrate an example of how our interface could help guide decisions for improving model performance. Further, we discuss how visualization can provide sanity checks during data preprocessing and model training. By closely examining the problem of glacier segmentation, we are able to discuss how visualization strategies can support the modeling process and the interpretation of prediction results from geospatial deep learning.

show abstract

Section: Interactive Visualizationmentioning

confidence: 99%

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

Zheng

Miao

Sankaran

2022

IJGI

View full text Add to dashboard Cite

show abstract

“…It also allows users to select combinations of scalar variables and user could interactively control time animation of data. In Tasnim and Mondal (2020), a data reduction method is proposed to reduce data size by 75% while preserving the visual elements of images.…”

Section: Interactive Visualizationmentioning

confidence: 99%

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

Zheng¹,

Miao²,

Sankaran³

2021

Preprint

View full text Add to dashboard Cite

Interpretability has attracted increasing attention in earth observation problems. We apply interactive visualization and representation analysis to guide interpretation of glacier segmentation models. We visualize the activations from a U-Net to understand and evaluate the model performance. We build an online interface using the Shiny R package to provide comprehensive error analysis of the predictions.Users can interact with the panels and discover model failure modes. Further, we discuss how visualization can provide sanity checks during data preprocessing and model training.

show abstract

ContourDiff: Revealing Differential Trends in Spatiotemporal Data

Ahmed

Beyene

Mondal

et al. 2021

2021 25th International Conference Information Visualisation (IV)

View full text Add to dashboard Cite

Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery

Cited by 5 publications

References 29 publications

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

ContourDiff: Revealing Differential Trends in Spatiotemporal Data

Contact Info

Product

Resources

About